COM6513 Natural Language Processing
|| This module provides an introduction to the field of
computer processing of written natural language, known as
Natural Language Processing (NLP). We will cover standard
theories, models and algorithms, discuss competing
solutions to problems, describe example systems and
applications, and highlight areas of open research. Students should be aware that there are limited places available on this course.
- Formal examination
||Dr Nikos Aletras
- to give students a well-rounded feel for the problems
and approaches of Statistical Natural Language
- to give students an understanding of the potential
areas of application of the techniques developed in
|| By the end of this course the students should:
- be able to describe and discuss the subareas of NLP
- be able to implement NLP algorithms and
- be able to describe and discuss the potential and
limitations of NLP techniques for applications such as
machine translation, question answering, information
retrieval and information extraction
Lectures will provide an overview of the field of NLP and
its sub-areas, and will introduce and explain its key
techniques, including their applicability and limitations.
In lab classes, students will practice implementing the
NLP techniques taught in class, testing their code in
application to real language data. Topics covered will
- N-gram Language Modelling
- Word Classes and Part-of-Speech Tagging
- Lexical Semantics, Word Sense Disambiguation and
- Syntactic and semantic parsing
- Information extraction
- Neural network architectures for NLP
||Students must have taken Text Processing (COM6115) in the previous semester and Machine Learning and Adaptive Intelligence (COM6509). The maximum number of students allowed on the module is 160.
|| There will be 2 formal lectures and 1 lab session per
|| Problem sheets will be set during labs sessions and then
will discussed in labs and/or lectures.
Verbal interaction during lectures.
- Daniel Jurafsky and James Martin. 2008. "Speech and
Language Processing" Prentice Hall. (A draft of the 3d edition can be found here: https://web.stanford.edu/~jurafsky/slp3/)
- Christopher D. Manning and Hinrich Schütze. 1999.
"Foundations of Statistical Natural Language Processing",
- Yoav Goldberg. 2017. Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies), Morgan & Claypool Publishers.